Arctic landfast ice extent and duration are examined from observations, ice assimilations,
ocean reanalyses and coupled models. From observations and
assimilations, it is shown that in areas where landfast ice conditions last
more than 5 months the first-year ice typically grows to more than 2 m and is
rarely less than 1 m. The observed spatial distribution of landfast ice
closely matches assimilation products but less so for ocean reanalyses and
coupled models. Although models generally struggle to represent the landfast
ice necessary to emulate the observed import/export of sea ice in regions
favourable to landfast ice conditions, some do exhibit both a realistic
climatology and a realistic decline of landfast ice extent under an
anthropogenic forcing scenario. In these more realistic simulations,
projections show that an extensive landfast ice cover should remain for at
least 5 months of the year, well into the end of the 21st century. This is in
stark contrast with the simulations that have an unrealistic emulation of
landfast ice conditions. In these simulations, slow and packed ice conditions
shrink markedly over the same period. In all simulations and in areas with
landfast ice that lasts more than 5 months, the end-of-winter sea ice thickness
remains between 1 and 2 m, well beyond the second half of the century. It is
concluded that in the current generation of climate models, projections of
winter sea ice conditions in the Canadian Arctic Archipelago and the Laptev
Sea are overly sensitive to the representation of landfast ice conditions and
that ongoing development in landfast ice parameterization will likely better
constrain these projections.

Sea ice that is immobile because it is attached to land is termed
“landfast”. In shallow coastal regions, large pressure ridges can be
anchored to the sea floor. These grounded ridges might then act as anchor
points to stabilize and maintain a landfast ice cover (Mahoney et al.,
2007). However, landfast ice is also present in some coastal regions that
are too deep for pressure ridges to become grounded. In this case, the ice
can stay in place due to the lateral propagation of internal ice stresses
that originate where the ice is in contact with the shore. Sea ice typically
becomes landfast if its keel extends all the way to the sea floor or if ice
stresses cannot overcome lateral friction at the coastline (Barry et al.,
1979). Most landfast ice melts or becomes mobile each summer.
Multi-year landfast ice (also termed an “ice-plug”) is rare but it is
known to occur within the Nansen Sound and Sverdrup Channel regions within
the Canadian Arctic Archipelago (CAA) (Serson, 1972, 1974). These ice-plugs
were a prominent feature within the CAA from the 1960s (Nansen Sound)
and 1970s (Sverdrup Channel) until they were both removed during the
anomalously warm summer of 1998 and have since rarely re-formed (Alt et al.,
2006). The disappearance of multi-year landfast ice is coincident with a
decline in pan-Arctic landfast ice extent of approximately
7 % decade−1
from 1976 to 2007 (Yu et al., 2014). Landfast ice has not only shrunk in
extent but has also thinned. While few long-term records of sea ice
thickness exist, they all show a thinning of springtime landfast ice. The
largest declines are generally found in the Barents Sea at 11 cm decade−1
(Gerland et al., 2008). Along the Russian coast and in the CAA, the thinning
has generally been less pronounced and is on average less than 5 cm decade−1
(Polyakov et al., 2010 for Russia, Howell et al., 2016 for Canada).

Landfast ice is immobile and, therefore, its maximum ice thickness is
primarily driven by thermodynamics from air temperature and the timing and
amount of snowfall during the growth period (Brown and Cote, 1992). Because
it isolates thermodynamics from import/export of sea ice, landfast ice is a
convenient bellwether of the effect of anthropogenic forcing on the Arctic
environment. This convenience has motivated several studies that
investigated the sensitivity of landfast ice to anthropogenic forcing in
both one-dimensional thermodynamic models (Flato and Brown, 1996; Dumas et
al., 2006) and CAA-focused regional three-dimensional ice-ocean coupled
models (e.g. Sou and Flato, 2009). Since the Sou and Flato (2009) study,
several high-resolution global ocean and sea ice models have become
available, thus making it possible to study the coupled response of landfast
ice to anthropogenic forcing. These models include the Community Earth
System Model Large Ensemble (CESM-LE), coupled climate models from the
Coupled Model Intercomparison Project phase 5 (CMIP5) and from the Ocean
Reanalysis Intercomparison Project (ORA-IP). Howell et al. (2016) provide a
preliminary investigation of the aforementioned climate models within the
CAA over a 50+ year record from 1957 to 2014 and found that they provide a
reasonable climatology but trends were unrealistic compared to observations.

In this study, we provide a more comprehensive investigation into the
variability of landfast ice extent and thickness from the current generation
of climate models for the Arctic-wide domain and also evaluate their
response to anthropogenic forcing. As climate models do not output a
dedicated landfast ice variable and as the ice velocity does not completely
vanish when landfast ice is simulated, we first develop an approach to
characterize landfast ice. We then describe the historical evolution of
landfast ice extent and springtime landfast ice thickness as well as their
future projections in models. Finally, we compare the coupled model
simulations with our own pan-Arctic ice-ocean simulations.

2.1 Observations

One of the longest records of landfast ice thickness and duration comes from
several coastal stations throughout Canada that date back to the late 1940s,
depending on the location. The data set is available online at the Canadian
Ice Service (CIS) website (http://www.ec.gc.ca/glaces-ice/, last access: 30 October 2018; see Archive
followed by ice thickness data). The thickness measurements are usually
performed weekly from freeze-up to break-up, as long as it is safe to walk on
the ice. For these reasons, the landfast ice duration at these stations,
measured as the number of weeks with measurements, is always biased on the
shorter side, possibly by a few weeks. From these station records, we
selected the four sites in the CAA that had continuous records up to 2015:
Alert, Eureka, Resolute and Cambridge Bay. From these weekly records
available from 1960 to 2015, we extracted the landfast ice duration and
springtime landfast ice thickness. A thorough analysis of these quantities
as derived from these records was initially presented by Brown and Cote (1992) from 1957 to 1989 and recently updated to 2014 by Howell et al. (2016).

For additional ice thickness information we use ice thickness surveys in
landfast regions of the CAA carried out by means of airborne electromagnetic
induction (AEM) sounding in 2011 and 2015 previously described in Haas and
Howell (2015). These surveys were averaged on a 25 km EASE 2.0 grid and are
shown in Fig. S1 in the Supplement. We also use weekly
ice thicknesses retrieved from CryoSat-2/SMOS in netCDF format for the
years 2010–2016, obtained from data.scienceportal.de and remapped using a
nearest-neighbour remapping to a 25 km EASE 2.0 grid. The resulting winter
maximum sea ice thicknesses are shown in Fig. S2 in the Supplement.

In order to spatially map landfast ice we use the National Ice Center (NIC)
ice charts products from the NSIDC (data set ID G02172) and ice charts from
the Canadian Ice Service Digital Archive (CISDA). The NIC ice charts are
available from 1972 to 2007 but we restrict the time period to 1980–2007 to
be consistent with CISDA. Indeed, the CISDA provide ice information before
1980 but landfast ice was not explicitly classified. We refer readers to
Tivy et al. (2011) (CISDA) and Yu et al. (2014) (NIC) for in-depth
descriptions of ice chart data and their validity as a climate record.
Following Galley et al. (2010), who also used the CIS ice chart data to map
landfast ice, we consider grid cells with sea ice concentration of
10/10ths
to be landfast. We defined pan-Arctic landfast extent using the NIC ice
charts (given their larger spatial domain) as the regions that are covered
by landfast ice for at least 1 month in the climatology. Both the NIC and
CISDA ice charts were converted from shape files to a 0.25∘
latitude–longitude grid and then converted using a nearest-neighbour
remapping to a 25 km Equal-Area Scalable Earth (EASE) 2.0 grid. We compute
the number of months (equivalent to “percent of the year” in Galley et
al., 2012) during which each grid cell was landfast for each time period from
September to August.

2.2 Models

Climate simulations and reanalyses do not provide a variable that explicitly
characterizes landfast ice conditions. This makes it challenging to verify
how it emulates landfast ice conditions compared to observations. To
circumvent this limitation, we use daily sea ice thickness (hereafter, sit),
sea ice concentration (hereafter, sic) and sea ice velocities (hereafter,
usi and vsi) to synthetically characterize landfast sea ice conditions using
the following procedure:

On the original model grid, we set the land mask to its nearest neighbour and
remap using a nearest-neighbour remapping usi, vsi and sit to the sic native
grid. Finally, we use a nearest-neighbour remapping to put all variables on a
EASE 2.0 grid.

The sea ice speed (hereafter, speedsi) is computed from usi and vsi on this
new grid.

Daily speedsi, sit and sic are averaged to weekly means.

A grid cell is identified as having “packed ice” if the remapped
weekly mean sic is larger than 85 %.

A grid cell is identified as having “slow ice” if the remapped weekly mean
speedsi is less than 1 cm s−1 (∼1 km day−1).

Slow, packed ice is used as a proxy for landfast ice.

At each grid cell we then compute the number of months in each year with
slow, packed ice. Using slow, packed ice is representative because we are
interested in one specific aspect of landfast ice: the fact that its growth
is primarily driven by thermodynamics and not by the import/export of sea
ice. This procedure is used with the Pan-Arctic Ice-Ocean Modeling and
Assimilation System (PIOMAS) (Zhang and Rothrock, 2003), a subset of the
highest-resolution models (see Table 3, Storto et al., 2011; Forget et al.,
2015; Haines et al., 2014; Zuo et al., 2015; Masina et al., 2015) from the
ORA-IP (Balsameda et al., 2015; Chevallier et al., 2017). Finally, we use the
CESM-LE and CMIP5 models to analyze climatological landfast ice extent and
thicknesses. Some ORA-IP models (ORAP5.0, UR025.4) do not provide daily
output. For these models, monthly data were first interpolated to daily
frequency and from then on the analysis was performed using the procedure
described above. It should be noted that sea ice velocities are not provided
by all models and only for a few simulations, constraining the scope of the
intercomparison presented here (see available models in Table 1). The data
for this study were retrieved from the ESGF using the cdb_query tool
(https://github.com/laliberte/cdb_query, last access: 9 April 2017).
Finally, the 1980–2005 historical experiment followed by the 2006–2015
Representative Concentration Pathway 8.5 (RCP85) experiment (Taylor et al.,
2012) are used with daily sea ice velocities, thickness and concentration.

Table 1Fraction of NIC landfast ice extent (magenta line in Fig. 2b) covered by slow, packed ice with a duration of more than 5
months for different models, regions and periods.

In the summer, the sea ice concentration drops below 100 % for some
models but it stills remains relatively high throughout the melt season. In
these models (e.g. NorESM1 and ACCESS1.0), the reduction in summer ice
concentration is not associated with increased sea ice speed (i.e. close to 0
correlation between the two variables over a year), unlike in the PIOMAS
product, where a strong anti-correlation between the two variables can be
measured. This suggests that these models may indeed have an ice
concentration below 100 % during the summer but the import/export of sea
ice remains quite limited because the packed ice never becomes mobile enough
in narrow channels, particularly within the CAA. As a result, one must
allow for some flexibility in the definition of packed ice in modelled
products and a number below 100 % needs to be chosen as a cut-off. Here,
we have chosen 85 % because (i) it represents landfast ice that ice grows
according to thermodynamics and not because of export/import and (ii) it is
widely accepted that in historical observational products a 15 %
uncertainty in sea ice concentration is to be expected. Since we are using
historical observation products in our comparison, we argue that the same
15 % uncertainty should be used when assessing model behaviour. We
acknowledge that, by using an 85 % ice concentration to define packed ice,
the lead fraction could be large at the boundary of the slow, packed ice, due
to the proximity of mobile ice. In these regions, the argument presented
above might break down. In this work, we will primarily focus on
archipelagoes and marginals seas where this is not an issue. It is, however,
important to keep in mind that, for applications that focus on those boundary
regions, this criterion might be too lenient.

The models listed above do not represent the grounding of pressure ridges.
Hence, they are not expected to perform well in regions where grounding is
known to be an important mechanism for the formation and stabilization of a
landfast ice cover. Observations show that grounding is important in the
Laptev Sea (Haas et al., 2005; Selyuzhenok et al., 2018), in the Beaufort Sea
(Mahoney et al., 2007) and in the Chukchi Sea (Mahoney et al.,
2014).
Nevertheless, these models can simulate landfast ice in some regions because
the models dynamics take into account the aforementioned mechanical
interactions. For most of these sea ice models, ice interactions are
represented by a viscous–plastic rheology with an elliptical yield curve
(Hibler, 1979).

Recently, a basal stress parameterization representing the effect of
grounding was developed (Lemieux et al., 2015). This parameterization
calculates, based on simulated ice conditions, the largest ridge(s) at each
grid point. When these subgrid-scale ridge(s) are able to reach the sea
floor, a basal (or seabed) stress term is added to the sea ice momentum
equation. This grounding scheme clearly improves the simulation of landfast
ice in regions such as the Alaskan coast, the Laptev Sea and the East
Siberian Sea. However, in deeper regions such as the Kara Sea, Lemieux et
al. (2015) pointed out that their model systematically underestimates the
area of landfast ice. As the grounding scheme is less active in these deeper
regions, Lemieux et al. (2016) modified the viscous–plastic rheology to
promote ice arching.

Following the work of Lemieux et al. (2016), we conducted simulations that
combined the grounding scheme and a modified viscous–plastic rheology. We
used the optimal parameters k1=8 and
k2=15 Nm−3 for the grounding scheme (Lemieux et al., 2015). Given a
certain mean thickness in a grid cell and a concentration, the grounding
scheme determines whether the parameterized ridges reach the sea floor or not
(which depends on k1) and defines the maximum seabed stress that can be
sustained by the grounded ridges (which is proportional to k2). As opposed
to the standard elliptical yield curve, the ellipse aspect ratio is set to
1.5 (instead of 2) and a small amount of isotropic tensile strength is used
(kt= 0.05).

For these simulations, we used the ocean model NEMO version 3.1 and the sea
ice model CICE version 4.0 with code modifications to include the grounding
scheme and to add tensile strength (Lemieux et al., 2016). Our 0.25∘
grid is a subset of the global ORCA mesh. It covers the Arctic Ocean, the
North Atlantic and the North Pacific. This ice-ocean prediction system, which
includes tides, was developed as part of the CONCEPTS (Canadian Operational
Network of Coupled Environmental PredicTion Systems) initiative. We refer to
our 0.25∘ model set-up and simulations as CREG025 (CONCEPTS-regional
0.25∘).

Note that, while adding the tides to our ice-ocean prediction systems, we
found that unrealistic sea thicknesses developed in late winter in tidally
active regions (e.g. Foxe Basin). To mitigate this problem, the Hibler (1979)
ice strength parameterization is used as opposed to the default
Rothrock (1975) formulation. The ice strength parameter P* was set to
27.5 k Nm−2 for our CREG025 simulation.

The sea ice model was initialized with sea ice thicknesses and concentrations
from the GLORYS2V1 ocean reanalyses. The ocean model was initialized by the
World Ocean Atlas (WOA13) climatology and forced at open boundaries by
GLORYS2V3 (Ferry et al., 2010; Chevallier et al., 2017). A spin-up from
October 2001 to September 2004 was performed. Free runs (no assimilation) are
then restarted from the fields in September 2004 and conducted up to the end
of 2010. The simulation was forced by 33 km Environment Canada atmospheric
reforecasts (Smith et al., 2014).

3.1 Landfast ice duration and thickness

The CAA is almost entirely covered by landfast ice for up to 8 months of the
year (i.e. November to July) (Canadian Ice Service, 2011) and is therefore a
useful region in which to begin evaluating a model representation of landfast ice
duration and thickness. Figure 1 illustrates the relationship between
landfast ice thickness and duration within the CAA for the observed data sets
(e.g. CryoSat-2, AEM and in situ) in addition to PIOMAS and CREG025. When
combining these heterogeneous data sources, a general picture of their
representativeness of ice thickness over landfast ice duration emerges. Based
on in situ observations landfast ice within the CAA lasts from 4 to ∼9
months and grows to ∼2 m, which is in agreement with previous studies (e.g.
Brown and Cote, 1992; Canadian Ice Service, 2011; Howell et al., 2016). For
PIOMAS, CREG025 and CryoSat-2 ice thickness standard deviations are close to
the variability observed at the in situ locations. However, very thick ice
upwards of ∼4 m is encountered at the 95th percentile in both the
CryoSat-2 and the PIOMAS data when the landfast ice lasts for more than 9
months. These very stable and thick landfast conditions are the result of
large multi-year ice floes, thus increasing the average ice thickness. It has
long been known that MYI forms in situ within the CAA, and very thick MYI from
the Arctic Ocean is also advected into the region (e.g. Melling, 2002), which
is evident from the airborne EM measurements thickness values (Haas and
Howell, 2015). This mix of ice types makes it challenging for models to
represent ice thickness within the CAA but, overall, they are in reasonable
agreement with observations.

Figure 1Canadian Arctic Archipelago (CAA) PIOMAS maximum ice thickness
against landfast ice duration from Canadian Ice Service (CIS) ice charts
over the 1980–2015 period (the mean is the thick red line, 95 one-sided
percentile is the red shading). In black, the same is shown for CryoSat2
instead of PIOMAS over the period 2010–2015 (see Fig. S1 in the Supplement for coverage). In
cyan, the same is shown for the operational model CREG025 instead of PIOMAS
over the years 2004–2010. In black squares, the same is shown for airborne
electromagnetic measurements in spring 2011 and 2015 over a small region of
the CAA (see Fig. S2 for coverage). In blue squares, the same in shown for
the in situ CIS Ice Monitoring programme at Cambridge Bay, Resolute Bay,
Eureka and Alert over the period 1980–2015.

3.2 Geographical distribution and climatology

The spatial distribution of annual landfast duration from observations (CIS
and NIC), PIOMAS and selected ocean reanalysis models is shown in Fig. 2.
Both ice charts products (CIS and NIC) show similar landfast ice extents and
durations in the CAA (Fig. 2a and b). This landfast ice extent is also very
similar in the two ice chart products over their regions of overlap (Fig. 2a
and b, magenta curve). In PIOMAS, the duration of slow and packed ice
conditions compares relatively well to the overall landfast extent and
duration in the ice chart products (Fig. 2c). There is, however, too much of
the slow and packed ice in the Beaufort Sea but too little in the Laptev and
Kara seas. Most ocean reanalysis products have a suitable representation of
slow, packed ice conditions in the CAA, the notable exception being CGLORS
and UR025.4 (not shown). In the CGLORS case, the ice component appears to
still be in spin-up at the beginning of the integration period because there
is an unphysical interannual variability in the first few years of the
simulation, and therefore results should not be expected to conform to
observations. In the UR025.4 case, winter ice is packed but is too mobile in
the Parry Channel and the M'Clintock Channel.

Figure 2(a) Historical landfast ice annual duration as reported in
the CIS ice charts. (b) Same as (a) but as reported in the
National Ice Center (NIC) ice charts. (c) Slow (<0.864 km day−1), packed (>85 % concentration) ice annual duration
as modelled by the assimilation product PIOMAS. (d)–(f) Same as
(c) but for different ocean reanalyses participating in the ORA-IP.
The landfast ice extent, calculated as the 1980–2007 average 1-month
landfast duration contour from NIC ice charts, is shown in magenta.

The spatial distribution of annual landfast ice duration in CMIP5 models with
higher resolution is illustrated in Fig. 3b–h. These models exhibit a
reasonable slow, packed ice extent and duration but it is mostly confined to
the CAA (Fig. 3b–h). The exception is the MRI-ESM1 (and applies to the other
models from the MRI), which simulates slow, packed ice conditions year-round
across the Arctic (not shown). This is likely due to its sea ice being
modelled as a simple viscous fluid without a sophisticated rheology. Compared
to the NIC analyses, all the CMIP5 models and reanalyses do not have enough
months of landfast ice on the Russian coast. GFDL-ESM2G , CESM-LE and PIOMAS
are the ones that provide the best landfast ice simulation in the Laptev,
Kara and East Siberian seas (Figs. 2c and 3d, f). Another important feature
of the import/export of sea ice in coupled models (ACCESS 1.0, CESM-LE,
GFDL-ESM2G) seems to be the tendency for many of them to emulate year-round or
close to year-round landfast ice in the Parry Channel regions of the CAA
(Fig. 3d, f, ACCESS 1.0 not shown). This is peculiar, since this would mean
that ice likely takes years to transit through the Parry Channel, allowing
thermodynamic forcing to create very thick ice in a region. Note that, in the
remaining models, the MIROC5 and MPI-ESM-MR both emulate a
landfast ice duration in the Parry Channel that is too short (Fig. 3c, e).

3.3 Trends in landfast ice duration

The largest observed negative trends in landfast ice duration of up to
1 month decade−1 is found primarily in the East Siberian Sea but a
general negative trend is located across the Arctic (Fig. 4a, b), as also
reported by Yu et al. (2014). In the CAA, trends are larger in the NIC ice
charts but both the CIS and NIC show relatively weak decline in duration in the
Parry Channel and the M'Clintock Channel. These relatively small trends are in stark
contrast with the very large trends almost everywhere in the CAA in the
PIOMAS simulations. For CGLORS, the model with sea ice still in spin-up,
there is a large positive increase in slow, packed ice duration (not shown).
Such increases are also seen in the Beaufort Sea in the GLORYS2V3 reanalysis,
indicating that towards the end of the reanalysis the Beaufort Sea is covered
by slow, packed ice for a few months per year (Fig. 4f). This is in complete
disagreement with observations and mandates that extra care should be taken when
using this product to analyze the import/export of sea ice in the Beaufort
Sea. In summary, reanalysis products appear to have a particularly difficult
time reproducing the long-term stability of the slow, packed ice
distribution, suggesting that targeted efforts to improve this aspect of
their import/export of sea ice are likely necessary.

Figure 4Same as Fig. 2 but for the trends in landfast ice duration over
the indicated period. Significant trends (p>0.05) are indicated
with stippling. Stippling was removed from some grid points to account for
the false discovery rate (Wilks, 2006).

CMIP5 models sea ice simulations (except the MRI models for the reason
explained above), on the other hand, fare relatively well at representing
negative trends in landfast ice duration when compared to observations
(Fig. 5). Most models tend to show an enhanced disappearance of slow, packed
conditions along the Beaufort Sea edge of the CAA and declines that are in
general agreement with observation in the Parry Channel. One exception is
the CESM-LE where some year-round slow, packed ice conditions do not
decline over the 1980–2015 period (Fig. 5d). The models with less slow,
packed ice than in observations, MIROC5 and MPI-ESM-MR, show relatively
strong declines that, if they continued, would indicate an almost complete
disappearance of slow, packed ice by the middle of the 21st century.

3.4 Regional evaluation of landfast ice extent and thickness

We now take a closer regional examination of landfast ice extent in the CAA,
Northwest Passage (Parry Channel route) and Laptev seas. These regions are
expected to experience increases in shipping activity from the middle to
late 21st century according to model simulations (Smith and Stephenson, 2013;
Melia et al., 2016). Instead of using an absolute measure of extent, we
report extent as a fraction of the ocean surface within the bounds of the NIC
1-month duration landfast ice extent climatology (magenta line in Fig. 2b).
This approach is necessary to appropriately compare observations to models
that represent the islands and channels of the CAA differently.

Over the 1980–2015 time period, landfast ice extent has declined
dramatically for durations longer than 5 months with a marked decline in the
extent of landfast ice with a 7 to 8 months duration within the Northwest
Passage (Fig. 6). What is, however, striking is how the extent of landfast ice
extent with duration of 5 months or less has been mostly constant over the
last 35 years (Fig. 6). It is because of this observation that we have
not included a trend analysis in Fig. 6. If the trend in landfast area
depends so strongly on landfast ice duration, it would probably be misleading
to attribute a hard number to the decline in landfast ice. If sea ice-albedo
feedback is an important player in recent sea ice decline (e.g. Perovich et
al., 2007) then it is not entirely surprising that during the polar night
landfast ice conditions re-establish themselves year after year, even in the
context of rapid Arctic warming. Finally, it is also worth noting that
Fig. 6a indicates that the small amounts of multi-year landfast ice within
the CAA have virtually disappeared in recent years (i.e. the 11-month line
has been at 0 since 2002), consistent with Alt et al. (2006).

Figure 6(a) Time series (5-year running-mean) of the fraction of
NIC landfast ice extent over the CAA (magenta line in Fig. 2b)
covered by landfast ice from CIS ice charts for more than the number of
months indicated by the line colour. (b) Same as (a) but over
the Northwest Passage.

Figure 7(a) Time series of the fraction of NIC landfast ice extent
(magenta line in Fig. 2b) covered by landfast ice (slow, packed ice
for PIOMAS and CREG025) with a duration of more than 5 months over the CAA.
(b) Same as (a) but over the Northwest Passage. (c) Same as (b) but over the Laptev Sea.

Landfast ice extent in the CAA and Northwest Passage is well represented in
the PIOMAS data assimilation product as it compares well with the CIS and NIC
ice chart products, although the NIC product does exhibit stronger
interannual variability (Fig. 7a, b). In the Laptev Sea, PIOMAS clearly
underestimates the area of landfast ice when compared to the NIC ice charts
(Fig. 7c). This is likely due to the fact that PIOMAS does not represent the
effect of grounding, an important mechanism for the formation and stability
of the Laptev Sea landfast ice cover (Selyuzhenok et al.,
2017). Despite this area of landfast ice
in the Laptev Sea being too small, PIOMAS exhibits a decline of ∼25 % of the
landfast extent over the last 35 years, which is consistent with the one from
the NIC ice charts.

Comparing current (1980–2015) to projected (2070–2080) landfast ice extent
from CMIP5 in these regions reveals considerable changes, which are summarized
in Table 1. The seven models with the smallest extent of 1979–2015 CAA slow,
packed ice (ACCESS1.0, ACCESS1.3, BCC-CSM1.1(m), GFDL-CM3, MIROC5,
MPI-ESM-LR, MPI-ESM-MR) lose most of it by 2070–2080, while the four models
with a large extent of 1979–2015 CAA slow, packed ice (CESM-LE, GFDL-ESM2G,
GFDL-ESM2M, NorESM1-M) retain most of it by 2070–2080. As mentioned earlier,
two models have unrealistic behaviour (MIR-ESM, MRI-CGCM3) because their sea
ice model is based on a simple perfect fluid.

Looking specifically in the CAA, current conditions (Fig. 8a, black) indicate
that the CMIP5 distribution is trimodal: one mode has an extent comparable
to observations (at 0.6 to 0.8 of NIC extent), a second mode has a much lower
extent (at 0.2–0.6 of NIC extent) and a third mode has an extent that covers
most of the area (∼1.0 of NIC extent). In the CAA, this trimodal
distribution yields a bimodal distribution in 2070–2080 projections
(Fig. 8a, yellow): one mode still has an extent comparable to observations
and a second mode has virtually no 5-month landfast ice extent left. In the
Northwest Passage, the story is much simpler (Fig. 8b). All considered models
are entirely covered with slow, packed ice conditions at least 5 months every
year for their historical simulations but in the 2070–2080 projections about
half become devoid of it, while the other half retain their historical
conditions. This highlights the difficulty of projecting how the import/export of
sea ice will react to anthropogenic forcing in the narrow channels of the
CAA. Finally, in the Laptev Sea, almost all considered models have little
slow, packed ice now and by 2070–2080 (Fig. 8c).

Figure 8(a) Distribution (across simulations and years) of the
fraction of NIC landfast ice extent (magenta line in Fig. 2b)
covered by slow, packed ice with a duration of more than 5 months over the
CAA for the 1980–2015 period in black and the 2070–2080 period of the RCP
8.5 scenario in yellow. (b) Same as (a) but over the
Northwest Passage. (c) Same as (b) but over the Laptev Sea.
(b)–(f) Same as (a)–(c) but for the CESM-LE. Note that in
(e)–(f) the highest bins go to 21 and 19, respectively. In red shading,
we identify the range of observations over the same period, as seen in
Fig. 7, disregarding PIOMAS in the Laptev Sea.

Figure 9(a) Distribution (across simulations and years) of the
annual maximum ice thickness averaged over landfast ice duration of more
than 5 months over the CAA for the 1980–2015 period in black and the
2070–2080 period of the RCP 8.5 scenario in yellow. (b) Same as
(a) but over the Northwest Passage. (c) Same as (b) but over the Laptev Sea.
(d)–(f) Same as (a)–(c) but for the CESM-LE.

The picture is generally clearer for the CESM-LE. In that model, the CAA and
the Northwest Passage has slow, packed ice comparable to observations
(Fig. 8d, e). In the projection, the CAA is expected to lose only 0.2 of its
slow, packed ice coverage and almost none in the Northwest Passage. In the
Laptev Sea, the CESM-LE is only performing marginally better that the CMIP5
multi-model ensemble and the projection shows the complete disappearance of
5-month slow, packed ice by 2070–2080 (Fig. 8f).

When we look at ice thickness, models show a wide range of ice thicknesses
over landfast ice during the 1980–2015 period for all regions (Fig. 9a–c).
However, for the 2070–2080 period they are essentially in agreement,
indicating that in all three regions landfast ice thickness is
found to grow between 1 and 2 m over the cold season (Fig. 9a–c). Moreover,
the projections indicate about a 0.5 m decrease in landfast ice thickness
towards the end of the 21st century. A similar growth range is apparent when
just looking at the CESM-LE but there is, however, a larger magnitude of
projected thickness decreases towards the end of the 21st century
(Fig. 9d–f).

3.5 Ice-ocean simulations with landfast ice parameterizations

The results we have presented so far have been focused on high-resolution
observational data sets, 25 km resolution reanalyses and coarser climate
models. From these different data sources we were able to demonstrate the
capabilities and limitations at emulating landfast ice conditions of models
of the current generation. In the remainder of this section, we will look at
our 6-year CREG025 simulations and see the benefits of using landfast ice
parameterizations.

As evident in Fig. 10, the CREG025 simulations show quite an accurate
representation of landfast ice duration in the Laptev Sea, the East Siberian
Sea and along the Alaskan Coast where grounding is crucial for simulating
landfast ice (Lemieux et al., 2015). The overestimation of landfast ice north
of the CAA is likely a consequence of our imperfect criterion for determining
whether the ice is landfast or not (slow-drifting ice for a NIC analyst can
be identified as landfast in our study).

Overall, in the CAA, the CREG025 landfast ice duration is in good agreement
with the ones of the NIC and CIS (Fig. 2a, b). In both NIC and CIS products,
the duration of landfast ice is small in tidally active regions such as the
Gulf of Boothia, Prince Regent Inlet, Lancaster Sound and Foxe Basin. In
accordance with observations, the CREG025 simulation (which includes explicit
tides) exhibits mobile ice in these regions throughout the winter
(Fig. 10b). However, CREG025 underestimates the landfast ice duration in
Barrow Strait and north of M'Clintock Channel.

We are currently conducting a thorough investigation of the impact of tides (and
the mechanisms involved) on simulated landfast ice. This will be the subject
of a future publication. Preliminary results suggest that including tides is
crucial to properly simulate landfast ice in certain regions of the CAA. We
speculate that the fact that many models (e.g. GFDL-ESM2G, CESM-LE, PIOMAS)
presented in this paper, overestimate landfast ice in parts of the CAA (e.g.
Gulf of Boothia and Prince Regent Inlet) is due to the absence of tides in
their simulations.

Looking at time series of 5-month landfast ice extent, the CREG025 simulation
follows observations very closely in the CAA and Laptev Sea (Fig. 7a, c). In
the Northwest Passage, however, the CREG025 simulation leads to too little
landfast ice (again due to the underestimation of landfast ice in Barrow
Strait and north of M'Clintock Channel). This could be due to the fact that our
CREG025 simulation seems to have ice thinner (and therefore weaker) than
observations (see Fig. 1). Overall, however, landfast ice extent in CREG025
is much more in line with observations in all three regions than most Earth
system models (shown in Fig. 8).

In this study, we have compared the geographical distribution of landfast ice
extent and duration in ocean reanalyses and coupled climate models to that in
historical ice charts. To achieve this comparison, we have used slow, packed
ice in models as a proxy for landfast ice. Using this proxy we find that some
current-generation models provide a reasonable representation of landfast ice
conditions (e.g. PIOMAS, CESM-LE and GFDL-ESM2G) but others still have a hard
time emulating landfast ice particularly in the CAA and even more so in the
Laptev Sea. Ice-ocean simulations with a grounding scheme and a modified
rheology to promote arching indicate that these parameterizations have the
capability to provide better projections for seasonal economic activities in
the Arctic. This is particularly important for reducing uncertainty in Arctic
shipping projections based on model simulations from the current generation
of models (e.g. Melia et al., 2016).

While many models do not emulate landfast ice accurately, their biases help
to explain why they project dramatic ice thickness decreases in the CAA,
which are not supported by long observational records (Howell et
al., 2016). Specifically, in regions with landfast ice, models tend to have
very thick ice in their historical simulations that is very sensitive to
anthropogenic forcing. Later in the 21st century, once multi-year ice
essentially disappears from the Arctic, the thickness distribution in models
becomes much more in line with the thickness expected from a simple
extrapolation of springtime landfast ice thickness records of less than ∼50 cm thinning over a century from typically ∼2 m springtime
thickness (Howell et al., 2016). This is also observed in the projections
analyzed in this study. Indeed, in the bulk of models and ensemble members in
regions where landfast ice lasts more than 5 months, the end-of-winter ice
thickness remains between 1 and 2 m until the end of the 21st century.

Finally, this analysis indicates that, although the sea ice cover is
projected to shrink for many months and in many regions (Laliberté et al.,
2016), landfast ice should cover most of the CAA for much of the winter well
past the middle of the century. This landfast ice should reasonably be expected to grow
to 1.5 m each winter, meaning that, by the time the ice breaks up, hazardous
ice floes should remain in the region for several weeks, if not months every
year. The presence of these hazardous ice floes during the months with the
most economic activity will likely have negative implications, especially for
shipping in the CAA. As a consequence, in order to deal with the annual
replenishing of thick sea ice in the CAA, ships will probably require
reinforced hulls to ward off environmental disasters as the shipping season
extends earlier.

Ice that forms over marginal seas often gets anchored and becomes landfast. Landfast ice is fundamental to the local ecosystems, is of economic importance as it leads to hazardous seafaring conditions and is also a choice hunting ground for both the local population and large predators. Using observations and climate simulations, this study shows that, especially in the Canadian Arctic, landfast ice might be more resilient to climate change than is generally thought.